Time -varying volatilities, CAPM betas, and factor loadings: A high -frequency data perspective - Zhang, Yibin (ProQuest Dissertations & Theses, 2003)
Betas or Factor Loadings in Multifactor Pricing models are the most fundamental risk measures of equity returns. I propose a nonparametric measure of latent betas—Realized Beta, or Realized Factor Loadings in multifactor pricing model. This idea originated from the emerging concept of realized volatility that has produced new insights in modeling financial market volatility: by summing squared intraday returns from tick-by-tick prices, it is possible to measure more accurately the ex-post volatility and covariance, and therefore the beta over a fixed time interval. The realized beta has three features: it is observable; it is time-varying, and it will converge to true beta as the sampling frequency becomes infinitesimal under standard assumptions. Therefore, the realized beta approach provides researchers and practitioners with a powerful method in measuring, modeling and forecasting betas. I apply the realized beta approach to two important subjects in financial economics: (1) News Asymmetry property of realized betas in Capital Asset Pricing Model and (2) Factor Representation and Return Forecasting in the Fama-French three-factor model. It has been long suspected that the conditional beta of an asset usually goes up after negative news hit the market—the so-called leverage effect. However, a host of studies using multivariate LARCH type models found little supporting evidence from the data. In the chapter titled “News Asymmetry in Volatility and CAPM Betas—a High Frequency Approach”, I tackle the beta asymmetry issue by using two approaches: the bivariate EGARCH model proposed in Braun, Nelson and Sunier (1995, Journal of Finance) and the realized beta approach. I find that the EGARCH model only identifies one industry (the construction sector) as exhibiting beta asymmetry at a daily level between 1993-2001; however, models built on realized beta reveal a systematic pattern of news asymmetry: the betas of cyclical industries (including construction) rise after bad news hits the market. Furthermore, compared to bivariate EGARCH dynamic betas, the high-frequency-based realized betas yield smaller in-sample pricing errors (alphas) for most of the 12 industry portfolios. In “Measuring and Modeling Systematic Risk in Factor Pricing Models using High-Frequency Data”, I construct realized factor loadings of the popular Fama-French three-factor model. Monthly realized factor loadings and returns are extracted from 5-minute intraday returns series of 25 size and book-to-market sorted portfolios between 1993–1999. Once again, the high-frequency based realized factor loadings outperform the conventional rolling regression loadings and constant loadings—not only in factor representations, but also in out-of-sample return predictions. In a mean-variance optimization experiment, the dynamic trading strategy based on realized factor loadings forecasts produces consistently higher Sharpe ratios than does the strategy based on conventional loading forecasts. Furthermore, with a quadratic utility function and risk aversion coefficient of 10, a representative investor is willing to pay up to 4.5% per year of returns in order to switch from the trading strategy of rolling regression forecast loadings to the ones using high-frequency-based realized factor loading forecasting.